Plot mapping to Visium data

General Setup

Setup chunk

Setup reticulate

knitr::opts_chunk$set(fig.width = 8)
knitr::opts_knit$set(root.dir = normalizePath(".."))
knitr::opts_knit$get("root.dir")

[1] "/nas/groups/treutlein/USERS/tomasgomes/projects/pallium_evo"

Load libraries

library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
py_available(initialize = FALSE)

[1] FALSE

use_python(Sys.which("python"))
py_config()

python:         /home/tpires/bin/miniconda3/bin/python
libpython:      /home/tpires/bin/miniconda3/lib/libpython3.8.so
pythonhome:     /home/tpires/bin/miniconda3:/home/tpires/bin/miniconda3
version:        3.8.3 (default, May 19 2020, 18:47:26)  [GCC 7.3.0]
numpy:          /home/tpires/bin/miniconda3/lib/python3.8/site-packages/numpy
numpy_version:  1.18.5

NOTE: Python version was forced by RETICULATE_PYTHON

Prepare data for cell2location

Load spatial data

library(Seurat)

Check data

visD = readRDS("/links/groups/treutlein/USERS/Ashley/projects/axolotl/experiments/20210201_axolotl_brain_52/data_processing/D1_113_sub_b_processed.RDS")

Save for reading into python

SpatialDimPlot(visD, group.by = "seurat_clusters")

SpatialFeaturePlot(visD, features = "nCount_Spatial")

Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.

Load cell2location results

Load results

test = read.csv("results/cell2loc/predictions_cell2loc.csv", header = T, row.names = 1)
visD = AddMetaData(visD, test)

Plot all individual scores

test = read.csv("results/cell2loc/predictions_cell2loc.csv", header = T, row.names = 1)
visD = AddMetaData(visD, test)

Combine scores

plotSpatialScores = function(obj, ct_use, img_name, colours = rainbow(length(ct_use))){
  names(colours) = ct_use
  
  # setup plotting data
  plot_df = obj@meta.data[,ct_use]
  plot_df = data.frame(apply(plot_df, 2, scales::rescale, to = c(0,1)))
  #for(n in ct_use) plot_df[,n][plot_df[,n]<.5] = 0
  coordinates = Seurat::GetTissueCoordinates(obj@images[[img_name]])
  data = cbind(coordinates, plot_df[rownames(x = coordinates),])
  
  plot = ggplot(data = data, aes_string(x = colnames(x = data)[2], 
                                        y = colnames(x = data)[1]))+
    Seurat:::geom_spatial(data = data, mapping = aes_string(alpha = ct_use[1]), point.size.factor = 1.6, 
                          image = obj@images[[img_name]], image.alpha = 0.5, colour = colours[1], shape = 19,
                          crop = T, stroke = .25)
  for(n in ct_use[-1]){
    plot = plot +
      Seurat:::geom_spatial(data = data, mapping = aes_string(alpha = n), point.size.factor = 1.6, 
                            image = obj@images[[img_name]], image.alpha = 0, colour = colours[n], shape = 19,
                            crop = T, stroke = .25)
  }
  
  plot = plot +
      labs(alpha = "Normalised score")+
      theme_classic()+
      theme(aspect.ratio = 1,
            axis.title = element_blank(),
            axis.ticks = element_blank(),
            axis.line = element_blank(),
            axis.text = element_blank())
  return(plot)
}

plotSpatialScores = function(obj, ct_use, img_name, groups = ct_use, colours = rainbow(length(ct_use))){
  names(colours) = ct_use
  
  # setup plotting data
  plot_df = obj@meta.data[,ct_use]
  plot_df = data.frame(apply(plot_df, 2, scales::rescale, to = c(0,1)))
  # for each group, get the top value
  if(!all(groups==ct_use)){
    for(g in names(groups)){
      plot_df[,g] = apply(plot_df[,groups[[g]]], 1, max)
    }
  } else{
    groups = lapply(ct_use, function(x) x)
    names(groups) = ct_use
  }
  plot_df$max_g = apply(plot_df[,names(groups)], 1, function(x) names(groups)[which.max(x)])
  rownames(plot_df) = rownames(obj@meta.data)
  
  coordinates = Seurat::GetTissueCoordinates(obj@images[[img_name]])
  data = cbind(coordinates, plot_df[rownames(x = coordinates),])
  
  plot = ggplot(data = data, aes_string(x = colnames(x = data)[2], 
                                        y = colnames(x = data)[1]))
  for(n in names(groups)){
    plot = plot +
      Seurat:::geom_spatial(data = data[data$max_g==n,], mapping = aes_string(alpha = n), point.size.factor = 2, 
                            image = obj@images[[img_name]], image.alpha = ifelse(n==names(groups)[1], 0.5, 0), 
                            colour = colours[n], shape = 19, crop = T, stroke = .25)
  }
  
  plot = plot +
      labs(alpha = "Normalised score")+
      theme_classic()+
      theme(aspect.ratio = 1,
            axis.title = element_blank(),
            axis.ticks = element_blank(),
            axis.line = element_blank(),
            axis.text = element_blank())
  return(plot)
}

plotSpatialScores = function(obj, ct_use, img_name, groups = ct_use, colours = rainbow(length(ct_use))){
  # setup plotting data
  plot_df = obj@meta.data[,ct_use]
  plot_df = data.frame(apply(plot_df, 2, scales::rescale, to = c(0,1)))
  # for each group, get the top value
  if(!all(groups==ct_use)){
    for(g in names(groups)){
      plot_df[,g] = apply(plot_df[,groups[[g]]], 1, max)
    }
  } else{
    groups = lapply(ct_use, function(x) x)
    names(groups) = ct_use
  }
  plot_df$max_g = apply(plot_df[,names(groups)], 1, function(x) names(groups)[which.max(x)])
  rownames(plot_df) = rownames(obj@meta.data)
  
  coordinates = Seurat::GetTissueCoordinates(obj@images[[img_name]])
  data = cbind(coordinates, plot_df[rownames(x = coordinates),])
  
  plot = ggplot(data = data, aes_string(x = colnames(x = data)[2], 
                                        y = colnames(x = data)[1]))
  plt_list = list()
  for(n in names(groups)){
    plt_list[[n]] = plot +
      Seurat:::geom_spatial(data = data, mapping = aes_string(alpha = n), point.size.factor = 4, 
                            image = obj@images[[img_name]], image.alpha = 0.5, 
                            colour = colours[n], shape = 19, crop = T, stroke = .25)
    plt_list[[n]] = plt_list[[n]] +
      labs(alpha = paste0("Max score\n", n))+
      scale_alpha_continuous(range = c(0,1))+
      theme_classic()+
      theme(aspect.ratio = 1,
            axis.title = element_blank(),
            axis.ticks = element_blank(),
            axis.line = element_blank(),
            axis.text = element_blank())
  }
  return(plt_list)
}



ct_use = c("glut_SUBSET_0", "glut_SUBSET_7", "glut_SUBSET_11", "glut_SUBSET_13",
           "glut_SUBSET_1", "glut_SUBSET_3",
           "glut_SUBSET_2", "glut_SUBSET_10", "glut_SUBSET_22")

reg_cols_simp = c("medial" = "#52168D", "dorsal" = "#C56007", "lateral" = "#118392")
reg_cols_simp = c(rep(reg_cols_simp[1], 4), rep(reg_cols_simp[2], 2), rep(reg_cols_simp[3], 3))

groups = list("medial" = c("glut_SUBSET_0", "glut_SUBSET_7", "glut_SUBSET_11", "glut_SUBSET_13"),
              "dorsal" = c("glut_SUBSET_1", "glut_SUBSET_3"),
              "lateral" = c("glut_SUBSET_2", "glut_SUBSET_10", "glut_SUBSET_22"))
names(reg_cols_simp) = ct_use
xxx = plotSpatialScores(obj = visD, ct_use = ct_use, img_name = "slice2", colours = reg_cols_simp)
cowplot::plot_grid(plotlist = xxx, ncol = 3)